Pandas II

Conditional Selection¶

Conditional selection allows us to select a subset of rows in a DataFrame that satisfy some specified condition.

To understand how to use conditional selection, we must look at another possible input of the .loc and [] methods – a boolean array, which is simply an array or Series where each element is either True or False. This boolean array must have a length equal to the number of rows in the DataFrame. It will return all rows that correspond to a value of True in the array. We used a very similar technique when performing conditional extraction from a Series in the last lecture.

To see this in action, let’s select all even-indexed rows in the first 10 rows of our DataFrame.

We can perform a similar operation using .loc:

These techniques worked well in this example, but you can imagine how tedious it might be to list out True and Falsefor every row in a larger DataFrame. To make things easier, we can instead provide a logical condition as an input to .loc or [] that returns a boolean array with the necessary length.

For example, to return all names associated with F sex:

Recall from the previous lecture that .head() will return only the first few rows in the DataFrame. In reality, babynames[logical operator] contains as many rows as there are entries in the original babynames DataFrame with sex "F".

Here, logical_operator evaluates to a Series of boolean values with length 407428.

print("There are a total of {} values in 'logical_operator'".format(len(logical_operator)))

Rows starting at row 0 and ending at row 239536 evaluate to True and are thus returned in the DataFrame. Rows from 239537 onwards evaluate to False and are omitted from the output.

print("The 0th item in this 'logical_operator' is: {}".format(logical_operator.iloc[0]))
print("The 239536th item in this 'logical_operator' is: {}".format(logical_operator.iloc[239536]))
print("The 239537th item in this 'logical_operator' is: {}".format(logical_operator.iloc[239537]))```

Passing a Series as an argument to babynames[] has the same effect as using a boolean array. In fact, the [] selection operator can take a boolean Series, array, and list as arguments. These three are used interchangeably throughout the course.

We can also use .loc to achieve similar results.

Boolean conditions can be combined using various bitwise operators, allowing us to filter results by multiple conditions. In the table below, p and q are boolean arrays or Series.

When combining multiple conditions with logical operators, we surround each individual condition with a set of parenthesis (). This imposes an order of operations on pandas evaluating your logic and can avoid code erroring.

For example, if we want to return data on all names with sex "F" born before the year 2000, we can write:

Note that we’re working with Series, so using and in place of &, or or in place | will error.

If we want to return data on all names with sex "F" or all born before the year 2000, we can write:

Boolean array selection is a useful tool, but can lead to overly verbose code for complex conditions. In the example below, our boolean condition is long enough to extend for several lines of code.

Fortunately, pandas provides many alternative methods for constructing boolean filters.

The .isin function is one such example. This method evaluates if the values in a Series are contained in a different sequence (list, array, or Series) of values. In the cell below, we achieve equivalent results to the DataFrame above with far more concise code.

The function str.startswith can be used to define a filter based on string values in a Series object. It checks to see if string values in a Series start with a particular character.

Adding, Removing, and Modifying Columns¶

In many data science tasks, we may need to change the columns contained in our DataFrame in some way. Fortunately, the syntax to do so is fairly straightforward.

To add a new column to a DataFrame, we use a syntax similar to that used when accessing an existing column. Specify the name of the new column by writing df["column"], then assign this to a Series or array containing the values that will populate this column.

If we need to later modify an existing column, we can do so by referencing this column again with the syntax df["column"], then re-assigning it to a new Series or array of the appropriate length.

We can rename a column using the .rename() method. It takes in a dictionary that maps old column names to their new ones.

If we want to remove a column or row of a DataFrame, we can call the .drop (documentation) method. Use the axis parameter to specify whether a column or row should be dropped. Unless otherwise specified, pandas will assume that we are dropping a row by default.

Notice that we re-assigned babynames to the result of babynames.drop(...). This is a subtle but important point: pandas table operations do not occur in-place. Calling df.drop(...) will output a copy of df with the row/column of interest removed without modifying the original df table.

In other words, if we simply call:

Useful Utility Functions¶

pandas contains an extensive library of functions that can help shorten the process of setting and getting information from its data structures. In the following section, we will give overviews of each of the main utility functions that will help us in Data 100.

Discussing all functionality offered by pandas could take an entire semester! We will walk you through the most commonly-used functions and encourage you to explore and experiment on your own.

• NumPy and built-in function support

• .shape

• .size

• .describe()

• .sample()

• .value_counts()

• .unique()

• .sort_values()

The pandas documentation will be a valuable resource in Data 100 and beyond.

NumPy¶

pandas is designed to work well with NumPy, the framework for array computations you encountered in Data 8. Just about any NumPy function can be applied to pandas DataFrames and Series.

.shape and .size¶

.shape and .size are attributes of Series and DataFrames that measure the “amount” of data stored in the structure. Calling .shape returns a tuple containing the number of rows and columns present in the DataFrame or Series. .size is used to find the total number of elements in a structure, equivalent to the number of rows times the number of columns.

Many functions strictly require the dimensions of the arguments along certain axes to match. Calling these dimension-finding functions is much faster than counting all of the items by hand.

.describe()¶

If many statistics are required from a DataFrame (minimum value, maximum value, mean value, etc.), then .describe() (documentation) can be used to compute all of them at once.

A different set of statistics will be reported if .describe() is called on a Series.

.sample()¶

As we will see later in the semester, random processes are at the heart of many data science techniques (for example, train-test splits, bootstrapping, and cross-validation). .sample() (documentation) lets us quickly select random entries (a row if called from a DataFrame, or a value if called from a Series).

By default, .sample() selects entries without replacement. Pass in the argument replace=True to sample with replacement.

Naturally, this can be chained with other methods and operators (iloc, etc.).

.value_counts()¶

The Series.value_counts() (documentation) method counts the number of occurrence of each unique value in a Series. In other words, it counts the number of times each unique value appears. This is often useful for determining the most or least common entries in a Series.

In the example below, we can determine the name with the most years in which at least one person has taken that name by counting the number of times each name appears in the "Name" column of babynames. Note that the return value is also a Series.

.unique()¶

If we have a Series with many repeated values, then .unique() (documentation) can be used to identify only the unique values. Here we return an array of all the names in babynames.

.sort_values()¶

Ordering a DataFrame can be useful for isolating extreme values. For example, the first 5 entries of a row sorted in descending order (that is, from highest to lowest) are the largest 5 values. .sort_values (documentation) allows us to order a DataFrame or Series by a specified column. We can choose to either receive the rows in ascending order (default) or descending order.

Unlike when calling .value_counts() on a DataFrame, we do not need to explicitly specify the column used for sorting when calling .value_counts() on a Series. We can still specify the ordering paradigm – that is, whether values are sorted in ascending or descending order.

Custom Sorts¶

Now, let’s try to solve a sorting problem using different approaches. Assume we want to find the longest baby names and sort our data accordingly.

We’ll start by loading the babynames dataset. Note that this dataset is filtered to only contain data from California.

# This code pulls census data and loads it into a DataFrame
# We won't cover it explicitly in this class, but you are welcome to explore it on your own
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)

import pandas as pd
import numpy as np
import urllib.request
import os.path
import zipfile

data_url = "https://www.ssa.gov/oact/babynames/state/namesbystate.zip"
local_filename = "data/babynamesbystate.zip"
if not os.path.exists(local_filename): # If the data exists don't download again
    with urllib.request.urlopen(data_url) as resp, open(local_filename, 'wb') as f:
        f.write(resp.read())

zf = zipfile.ZipFile(local_filename, 'r')

ca_name = 'STATE.CA.TXT'
field_names = ['State', 'Sex', 'Year', 'Name', 'Count']
with zf.open(ca_name) as fh:
    babynames = pd.read_csv(fh, header=None, names=field_names)

babynames.tail(10)

Approach 1: Create a Temporary Column¶

One method to do this is to first start by creating a column that contains the lengths of the names.

We can then sort the DataFrame by that column using .sort_values():

Finally, we can drop the name_length column from babynames to prevent our table from getting cluttered.

Approach 2: Sorting using the key Argument¶

Another way to approach this is to use the key argument of .sort_values(). Here we can specify that we want to sort "Name" values by their length.

Approach 3: Sorting using the map Function¶

We can also use the map function on a Series to solve this. Say we want to sort the babynames table by the number of "dr"'s and "ea"'s in each "Name". We’ll define the function dr_ea_count to help us out.

We can drop the dr_ea_count once we’re done using it to maintain a neat table.

Parting Note¶

Manipulating DataFrames is not a skill that is mastered in just one day. Due to the flexibility of pandas, there are many different ways to get from point A to point B. We recommend trying multiple different ways to solve the same problem to gain even more practice and reach that point of mastery sooner.

Next, we will start digging deeper into the mechanics behind grouping data.